Debugging agents can be complex! In my limited experience, agents interacting with each other tend to drift from their core mission. How do you keep them on track?
Debugging multi-agent drift is like debugging a distributed system - you need visibility into what each agent is thinking and doing.
Log everything with timestamps. Not just final outputs, but every decision, every piece of information received, and every action taken. You need to reconstruct the sequence of events that led to drift.
Agent introspection. Have each agent periodically dump its current understanding: "What do I think my goal is right now? What information am I basing decisions on?" This reveals when their mental model diverges.
Conversation replay. Build the ability to replay agent interactions step by step. Often the drift happens in a specific exchange that looks fine in isolation but breaks the context.
State snapshots. Capture the shared state at regular intervals. You can then diff these snapshots to see exactly when and how the system state diverged from expectations.
Trace correlation IDs. Tag every interaction with a unique ID that flows through the entire agent chain. This lets you follow a single request through the whole system.
Agent dependency graphs. Visualize which agents are influencing which others. Drift often shows up as unexpected dependencies or circular influences.
The key insight: you can't debug what you can't see. Multi-agent systems are black boxes by default, so you have to build extensive observability from day one.
Most people try to debug by looking at final outputs, but the real problems are usually buried in the middle interactions.
This is super helpful, thank you! You’ve definitely given me some new strategies to try. Conceptually, it sounds like tracing a game of telephone—except with agents. 😆
Debugging agents can be complex! In my limited experience, agents interacting with each other tend to drift from their core mission. How do you keep them on track?
that is a great question.
Debugging multi-agent drift is like debugging a distributed system - you need visibility into what each agent is thinking and doing.
Log everything with timestamps. Not just final outputs, but every decision, every piece of information received, and every action taken. You need to reconstruct the sequence of events that led to drift.
Agent introspection. Have each agent periodically dump its current understanding: "What do I think my goal is right now? What information am I basing decisions on?" This reveals when their mental model diverges.
Conversation replay. Build the ability to replay agent interactions step by step. Often the drift happens in a specific exchange that looks fine in isolation but breaks the context.
State snapshots. Capture the shared state at regular intervals. You can then diff these snapshots to see exactly when and how the system state diverged from expectations.
Trace correlation IDs. Tag every interaction with a unique ID that flows through the entire agent chain. This lets you follow a single request through the whole system.
Agent dependency graphs. Visualize which agents are influencing which others. Drift often shows up as unexpected dependencies or circular influences.
The key insight: you can't debug what you can't see. Multi-agent systems are black boxes by default, so you have to build extensive observability from day one.
Most people try to debug by looking at final outputs, but the real problems are usually buried in the middle interactions.
This is super helpful, thank you! You’ve definitely given me some new strategies to try. Conceptually, it sounds like tracing a game of telephone—except with agents. 😆
😆😆
Love this. Totally agree. Whether it’s a machine learning model or a group of people, the structure of the team is everything.
the structure, and most importantly, the communication
My friend explored this topic pretty profoundly, take a look at these articles:
https://trilogyai.substack.com/p/ai-discovery
https://trilogyai.substack.com/p/ai-ping-pong